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Georgia Tech Men’s Soccer Club, Tau Beta Pi, Pi Tai Sigma, National Society of Collegiate Scholars

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If we knew what it was we were doing, it would not be called research, would it? - Albert Einstein

My career aspiration is to be recognized as a leading researcher in the field of mechanical engineering. I have always wanted to have an impact on the world, and I feel that by becoming a researcher, I can develop innovative technologies, products and processes which will benefit society as a whole.

Design decisions under uncertainty.

Publication: Schlosser, J., and Paredis, C. J. J., 2007, "Managing Multiple Sources of Epistemic Uncertainty in Engineering Decision Making." Society of Automotive Engineering World Congress, April 16-19, Detroit, Michigan.

Georgia Tech’s research program was one of my best experiences as an undergraduate. During my five semesters of research, I learned how to work independently, propose a research question and hypothesis, construct simulations and extract results, present my work to graduate students and professors, and clearly and concisely communicate my ideas through a formal research paper. Most importantly, I learned the joys of working on a unique project and adding new knowledge to the engineering community. Research at Georgia Tech has sparked my interest in pursuing a PhD in mechanical engineering and has given me the confidence necessary for an innovative career in research and development.

Managing uncertainty is an integral part of making well-informed engineering decisions. When formulating a design problem, many of the variables and models contain epistemic uncertainty—uncertainty due to lack of knowledge. If this lack of knowledge is significant, it may be advantageous to acquire additional information before making a design decision. This leads to a research question about meta-level information management: How much and which information should be acquired in support of a design decision? This is not a trivial question, because it involves a value trade-off: acquiring information requires resources and should therefore only be considered if the expected value––in terms of an improvement in the outcome of the decision––exceeds the cost. My professor, Dr. Chris Paredis, and I formulated a framework for modeling the cost and benefit of gathering information to characterize multiple sources of uncertainty in engineering design problems. We used a criterion called the expected net value of information, defined as the value gained by using the information minus the cost of gathering the information, to determine the appropriate amount of information to acquire in support of design decisions. After programmig the framework in Matlab and running computational simulations on an electric vehicle decision model, I found that our method yielded over ten dollars in risk reduction for every one dollar spent acquiring information for the design decision. Chris and I are currently working to improve our framework by making it more robust to varying engineering decision problems.